Overview

Dataset statistics

Number of variables12
Number of observations2014
Missing cells43
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory438.2 KiB
Average record size in memory222.8 B

Variable types

Categorical2
Numeric10

Alerts

Country name has a high cardinality: 152 distinct values High cardinality
Ladder score is highly correlated with Logged GDP per capita and 4 other fieldsHigh correlation
Logged GDP per capita is highly correlated with Ladder score and 2 other fieldsHigh correlation
Social support is highly correlated with Ladder score and 2 other fieldsHigh correlation
Healthy life expectancy is highly correlated with Ladder score and 2 other fieldsHigh correlation
Freedom to make life choices is highly correlated with Ladder score and 1 other fieldsHigh correlation
Positive affect is highly correlated with Ladder score and 1 other fieldsHigh correlation
Ladder score is highly correlated with Logged GDP per capita and 4 other fieldsHigh correlation
Logged GDP per capita is highly correlated with Ladder score and 2 other fieldsHigh correlation
Social support is highly correlated with Ladder score and 2 other fieldsHigh correlation
Healthy life expectancy is highly correlated with Ladder score and 2 other fieldsHigh correlation
Freedom to make life choices is highly correlated with Ladder score and 1 other fieldsHigh correlation
Positive affect is highly correlated with Ladder score and 1 other fieldsHigh correlation
Ladder score is highly correlated with Logged GDP per capita and 2 other fieldsHigh correlation
Logged GDP per capita is highly correlated with Ladder score and 2 other fieldsHigh correlation
Social support is highly correlated with Ladder score and 1 other fieldsHigh correlation
Healthy life expectancy is highly correlated with Ladder score and 1 other fieldsHigh correlation
Regional indicator is highly correlated with Ladder score and 7 other fieldsHigh correlation
Ladder score is highly correlated with Regional indicator and 6 other fieldsHigh correlation
Logged GDP per capita is highly correlated with Regional indicator and 7 other fieldsHigh correlation
Social support is highly correlated with Regional indicator and 6 other fieldsHigh correlation
Healthy life expectancy is highly correlated with Regional indicator and 5 other fieldsHigh correlation
Freedom to make life choices is highly correlated with Regional indicator and 6 other fieldsHigh correlation
Generosity is highly correlated with Regional indicator and 1 other fieldsHigh correlation
Perceptions of corruption is highly correlated with Regional indicator and 5 other fieldsHigh correlation
Positive affect is highly correlated with Regional indicator and 5 other fieldsHigh correlation
Negative affect is highly correlated with Positive affectHigh correlation
Regional indicator has 43 (2.1%) missing values Missing
Ladder score has unique values Unique
Social support has unique values Unique

Reproduction

Analysis started2022-07-13 22:39:48.216354
Analysis finished2022-07-13 22:40:00.101332
Duration11.88 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Country name
Categorical

HIGH CARDINALITY

Distinct152
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size128.5 KiB
Zimbabwe
 
16
Mexico
 
16
Russia
 
16
Chile
 
16
Colombia
 
16
Other values (147)
1934 

Length

Max length24
Median length7
Mean length8.249255214
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAfghanistan
2nd rowAfghanistan
3rd rowAfghanistan
4th rowAfghanistan
5th rowAfghanistan

Common Values

ValueCountFrequency (%)
Zimbabwe16
 
0.8%
Mexico16
 
0.8%
Russia16
 
0.8%
Chile16
 
0.8%
Colombia16
 
0.8%
Denmark16
 
0.8%
Dominican Republic16
 
0.8%
Ecuador16
 
0.8%
Egypt16
 
0.8%
El Salvador16
 
0.8%
Other values (142)1854
92.1%

Length

2022-07-13T19:40:00.154062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
united46
 
1.9%
republic34
 
1.4%
south32
 
1.3%
and19
 
0.8%
congo19
 
0.8%
zimbabwe16
 
0.7%
turkey16
 
0.7%
bolivia16
 
0.7%
argentina16
 
0.7%
venezuela16
 
0.7%
Other values (164)2156
90.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Regional indicator
Categorical

HIGH CORRELATION
MISSING

Distinct10
Distinct (%)0.5%
Missing43
Missing (%)2.1%
Memory size152.5 KiB
Sub-Saharan Africa
426 
Latin America and Caribbean
299 
Western Europe
284 
Middle East and North Africa
228 
Central and Eastern Europe
227 
Other values (5)
507 

Length

Max length34
Median length21
Mean length21.46067986
Min length9

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSouth Asia
2nd rowSouth Asia
3rd rowSouth Asia
4th rowSouth Asia
5th rowSouth Asia

Common Values

ValueCountFrequency (%)
Sub-Saharan Africa426
21.2%
Latin America and Caribbean299
14.8%
Western Europe284
14.1%
Middle East and North Africa228
11.3%
Central and Eastern Europe227
11.3%
Commonwealth of Independent States171
8.5%
Southeast Asia125
 
6.2%
South Asia89
 
4.4%
North America and ANZ62
 
3.1%
East Asia60
 
3.0%
(Missing)43
 
2.1%

Length

2022-07-13T19:40:00.248783image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-07-13T19:40:00.309326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
and816
13.3%
africa654
 
10.6%
europe511
 
8.3%
sub-saharan426
 
6.9%
america361
 
5.9%
latin299
 
4.9%
caribbean299
 
4.9%
north290
 
4.7%
east288
 
4.7%
western284
 
4.6%
Other values (11)1916
31.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

year
Real number (ℝ≥0)

Distinct17
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.765641
Minimum2005
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2022-07-13T19:40:00.417721image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2005
5-th percentile2006
Q12010
median2014
Q32018
95-th percentile2021
Maximum2021
Range16
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.49912407
Coefficient of variation (CV)0.002234184544
Kurtosis-1.076824403
Mean2013.765641
Median Absolute Deviation (MAD)4
Skewness-0.09866183816
Sum4055724
Variance20.2421174
MonotonicityNot monotonic
2022-07-13T19:40:00.493887image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2021143
 
7.1%
2017142
 
7.1%
2011139
 
6.9%
2019139
 
6.9%
2014138
 
6.9%
2018137
 
6.8%
2015136
 
6.8%
2016135
 
6.7%
2012134
 
6.7%
2013133
 
6.6%
Other values (7)638
31.7%
ValueCountFrequency (%)
200527
 
1.3%
200686
4.3%
200799
4.9%
2008107
5.3%
2009108
5.4%
2010119
5.9%
2011139
6.9%
2012134
6.7%
2013133
6.6%
2014138
6.9%
ValueCountFrequency (%)
2021143
7.1%
202092
4.6%
2019139
6.9%
2018137
6.8%
2017142
7.1%
2016135
6.7%
2015136
6.8%
2014138
6.9%
2013133
6.6%
2012134
6.7%

Ladder score
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct2014
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.47369654
Minimum2.375091791
Maximum8.01893425
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2022-07-13T19:40:00.589582image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2.375091791
5-th percentile3.675445497
Q14.634406567
median5.386819601
Q36.309128642
95-th percentile7.392767453
Maximum8.01893425
Range5.643842459
Interquartile range (IQR)1.674722075

Descriptive statistics

Standard deviation1.126334817
Coefficient of variation (CV)0.2057722435
Kurtosis-0.7301574873
Mean5.47369654
Median Absolute Deviation (MAD)0.8224008083
Skewness0.06237489466
Sum11024.02483
Variance1.268630121
MonotonicityNot monotonic
2022-07-13T19:40:00.688208image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.7235898971
 
< 0.1%
4.6396474841
 
< 0.1%
7.4444708821
 
< 0.1%
7.6782770161
 
< 0.1%
7.6322875021
 
< 0.1%
7.4156823161
 
< 0.1%
5.1012001041
 
< 0.1%
5.0536642071
 
< 0.1%
5.0154852871
 
< 0.1%
5.2398347851
 
< 0.1%
Other values (2004)2004
99.5%
ValueCountFrequency (%)
2.3750917911
< 0.1%
2.5229001051
< 0.1%
2.661718131
< 0.1%
2.6875529291
< 0.1%
2.6930611131
< 0.1%
2.6935231691
< 0.1%
2.6943032741
< 0.1%
2.7015912531
< 0.1%
2.8078551291
< 0.1%
2.838958741
< 0.1%
ValueCountFrequency (%)
8.018934251
< 0.1%
7.9708919531
< 0.1%
7.8893499371
< 0.1%
7.858107091
< 0.1%
7.8421001431
< 0.1%
7.8342332841
< 0.1%
7.7882518771
< 0.1%
7.788231851
< 0.1%
7.7803478241
< 0.1%
7.7762088781
< 0.1%

Logged GDP per capita
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1995
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.361088469
Minimum6.635322094
Maximum11.64816856
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2022-07-13T19:40:00.788786image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum6.635322094
5-th percentile7.369505835
Q18.445964336
median9.46169281
Q310.35939288
95-th percentile10.92168541
Maximum11.64816856
Range5.01284647
Interquartile range (IQR)1.913428545

Descriptive statistics

Standard deviation1.159062399
Coefficient of variation (CV)0.1238170542
Kurtosis-0.9003031859
Mean9.361088469
Median Absolute Deviation (MAD)0.9632906914
Skewness-0.3109951131
Sum18853.23218
Variance1.343425644
MonotonicityNot monotonic
2022-07-13T19:40:00.889369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.4983879443
 
0.1%
9.4487793263
 
0.1%
10.75620013
 
0.1%
9.1862010962
 
0.1%
8.4090064122
 
0.1%
7.5784368522
 
0.1%
8.0550387992
 
0.1%
9.4481439592
 
0.1%
9.5843744282
 
0.1%
6.6353220942
 
0.1%
Other values (1985)1991
98.9%
ValueCountFrequency (%)
6.6353220942
0.1%
6.6782274251
< 0.1%
6.7187623981
< 0.1%
6.7233085631
< 0.1%
6.7281641961
< 0.1%
6.741916181
< 0.1%
6.7481760981
< 0.1%
6.7758231161
< 0.1%
6.785016061
< 0.1%
6.7869830131
< 0.1%
ValueCountFrequency (%)
11.648168561
< 0.1%
11.646564481
< 0.1%
11.644917491
< 0.1%
11.640029911
< 0.1%
11.633571621
< 0.1%
11.616852761
< 0.1%
11.598289491
< 0.1%
11.594555851
< 0.1%
11.591707231
< 0.1%
11.579789161
< 0.1%

Social support
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct2014
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8127426532
Minimum0.29018417
Maximum0.9873434901
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2022-07-13T19:40:00.988973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.29018417
5-th percentile0.5689602554
Q10.746827662
median0.8366412818
Q30.9060654044
95-th percentile0.9500172824
Maximum0.9873434901
Range0.6971593201
Interquartile range (IQR)0.1592377424

Descriptive statistics

Standard deviation0.1189236691
Coefficient of variation (CV)0.1463238931
Kurtosis1.108554122
Mean0.8127426532
Median Absolute Deviation (MAD)0.07576361299
Skewness-1.100305547
Sum1636.863704
Variance0.01414283906
MonotonicityNot monotonic
2022-07-13T19:40:01.089550image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.45066231491
 
< 0.1%
0.79830503461
 
< 0.1%
0.94116193061
 
< 0.1%
0.94765740631
 
< 0.1%
0.93587893251
 
< 0.1%
0.95851129291
 
< 0.1%
0.80506110191
 
< 0.1%
0.7503741981
 
< 0.1%
0.81463849541
 
< 0.1%
0.84891515971
 
< 0.1%
Other values (2004)2004
99.5%
ValueCountFrequency (%)
0.290184171
< 0.1%
0.29093381761
< 0.1%
0.29133367541
< 0.1%
0.3029550911
< 0.1%
0.3195891381
< 0.1%
0.32569253441
< 0.1%
0.3729078771
< 0.1%
0.38237351181
< 0.1%
0.38739091161
< 0.1%
0.41997286681
< 0.1%
ValueCountFrequency (%)
0.98734349011
< 0.1%
0.9849400521
< 0.1%
0.98328608271
< 0.1%
0.98293787241
< 0.1%
0.98252171281
< 0.1%
0.98182457691
< 0.1%
0.98028320071
< 0.1%
0.97896528241
< 0.1%
0.97883975511
< 0.1%
0.97742956881
< 0.1%

Healthy life expectancy
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct946
Distinct (%)47.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.49355799
Minimum32.29999924
Maximum77.09999847
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2022-07-13T19:40:01.195989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum32.29999924
5-th percentile49.13299923
Q158.72174931
median65.23999786
Q368.69999695
95-th percentile73.03399696
Maximum77.09999847
Range44.79999924
Interquartile range (IQR)9.978247643

Descriptive statistics

Standard deviation7.460396925
Coefficient of variation (CV)0.1174984858
Kurtosis-0.0335410296
Mean63.49355799
Median Absolute Deviation (MAD)4.829998016
Skewness-0.7420575934
Sum127876.0258
Variance55.65752227
MonotonicityNot monotonic
2022-07-13T19:40:01.289733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72.1999969516
 
0.8%
7315
 
0.7%
66.4000015314
 
0.7%
67.1999969514
 
0.7%
66.5999984713
 
0.6%
72.5999984712
 
0.6%
66.8000030512
 
0.6%
72.4000015312
 
0.6%
66.3000030511
 
0.5%
65.1999969511
 
0.5%
Other values (936)1884
93.5%
ValueCountFrequency (%)
32.299999241
< 0.1%
36.860000611
< 0.1%
40.299999241
< 0.1%
40.380001071
< 0.1%
40.808292391
< 0.1%
40.900001531
< 0.1%
41.200000761
< 0.1%
41.419998171
< 0.1%
41.580001831
< 0.1%
42.099998471
< 0.1%
ValueCountFrequency (%)
77.099998471
< 0.1%
76.952857971
< 0.1%
76.800003051
< 0.1%
76.51
< 0.1%
76.199996951
< 0.1%
75.900001531
< 0.1%
75.680000311
< 0.1%
75.459999081
< 0.1%
75.199996951
< 0.1%
75.100440981
< 0.1%

Freedom to make life choices
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2005
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7454708345
Minimum0.2575338185
Maximum0.9851777554
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2022-07-13T19:40:01.389335image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.2575338185
5-th percentile0.4841006339
Q10.6505935639
median0.7655630112
Q30.8590308279
95-th percentile0.935854423
Maximum0.9851777554
Range0.7276439369
Interquartile range (IQR)0.208437264

Descriptive statistics

Standard deviation0.1404370849
Coefficient of variation (CV)0.188387095
Kurtosis-0.1153692999
Mean0.7454708345
Median Absolute Deviation (MAD)0.1033967137
Skewness-0.6197164774
Sum1501.378261
Variance0.01972257482
MonotonicityNot monotonic
2022-07-13T19:40:01.487962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.78069173373
 
0.1%
0.88730161893
 
0.1%
0.74320713542
 
0.1%
0.8320024612
 
0.1%
0.9355429512
 
0.1%
0.51339577352
 
0.1%
0.683557572
 
0.1%
0.64095264671
 
< 0.1%
0.55217373371
 
< 0.1%
0.51318395141
 
< 0.1%
Other values (1995)1995
99.1%
ValueCountFrequency (%)
0.25753381851
< 0.1%
0.26006931071
< 0.1%
0.28681439161
< 0.1%
0.29461178181
< 0.1%
0.30354040861
< 0.1%
0.30613189941
< 0.1%
0.31456461551
< 0.1%
0.33243611451
< 0.1%
0.33331209421
< 0.1%
0.3352236451
< 0.1%
ValueCountFrequency (%)
0.98517775541
< 0.1%
0.98380303381
< 0.1%
0.97993713621
< 0.1%
0.97113502031
< 0.1%
0.97029453521
< 0.1%
0.970130981
< 0.1%
0.96989798551
< 0.1%
0.96978837251
< 0.1%
0.96858048441
< 0.1%
0.96456110481
< 0.1%

Generosity
Real number (ℝ)

HIGH CORRELATION

Distinct1979
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.002602626237
Minimum-0.3350402415
Maximum0.6980987787
Zeros0
Zeros (%)0.0%
Negative1157
Negative (%)57.4%
Memory size15.9 KiB
2022-07-13T19:40:01.600260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.3350402415
5-th percentile-0.2272922955
Q1-0.1160833836
median-0.02883870527
Q30.08672024682
95-th percentile0.301271151
Maximum0.6980987787
Range1.03313902
Interquartile range (IQR)0.2028036304

Descriptive statistics

Standard deviation0.1605263059
Coefficient of variation (CV)-61.6785859
Kurtosis0.9636199513
Mean-0.002602626237
Median Absolute Deviation (MAD)0.1001072414
Skewness0.8450921738
Sum-5.241689242
Variance0.0257686949
MonotonicityNot monotonic
2022-07-13T19:40:01.699862image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.17370867525
 
0.2%
-0.05837038524
 
0.2%
-0.16453250984
 
0.2%
0.065712652293
 
0.1%
-0.0056741323893
 
0.1%
-0.12805592583
 
0.1%
0.089027248663
 
0.1%
-0.16252174972
 
0.1%
0.028112838042
 
0.1%
-0.13679705562
 
0.1%
Other values (1969)1983
98.5%
ValueCountFrequency (%)
-0.33504024151
< 0.1%
-0.31643930081
< 0.1%
-0.30656155941
< 0.1%
-0.30501219631
< 0.1%
-0.30490773921
< 0.1%
-0.3032038511
< 0.1%
-0.30287697911
< 0.1%
-0.29636645321
< 0.1%
-0.29509571191
< 0.1%
-0.29305177931
< 0.1%
ValueCountFrequency (%)
0.69809877871
< 0.1%
0.68931800131
< 0.1%
0.68756020071
< 0.1%
0.67942637211
< 0.1%
0.65000909571
< 0.1%
0.64497506621
< 0.1%
0.56113839151
< 0.1%
0.55534803871
< 0.1%
0.55252140761
< 0.1%
0.54155296091
< 0.1%

Perceptions of corruption
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1956
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7431455168
Minimum0.03519798815
Maximum0.9832760096
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2022-07-13T19:40:01.911763image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.03519798815
5-th percentile0.3224171698
Q10.6844315976
median0.7984966338
Q30.8670773506
95-th percentile0.9395506233
Maximum0.9832760096
Range0.9480780214
Interquartile range (IQR)0.182645753

Descriptive statistics

Standard deviation0.1847283093
Coefficient of variation (CV)0.2485762278
Kurtosis1.840528489
Mean0.7431455168
Median Absolute Deviation (MAD)0.08676663041
Skewness-1.472708633
Sum1496.695071
Variance0.03412454826
MonotonicityNot monotonic
2022-07-13T19:40:02.014295image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.535551063712
 
0.6%
0.697379350711
 
0.5%
0.371624283510
 
0.5%
0.55721066597
 
0.3%
0.58130836496
 
0.3%
0.8238995916
 
0.3%
0.66659292585
 
0.2%
0.78581309324
 
0.2%
0.82237717513
 
0.1%
0.6995855572
 
0.1%
Other values (1946)1948
96.7%
ValueCountFrequency (%)
0.035197988151
< 0.1%
0.047311153261
< 0.1%
0.060282066461
< 0.1%
0.063614882531
< 0.1%
0.065775275231
< 0.1%
0.069619603461
< 0.1%
0.078000180421
< 0.1%
0.081324897711
< 0.1%
0.081958577041
< 0.1%
0.094604469841
< 0.1%
ValueCountFrequency (%)
0.98327600961
< 0.1%
0.98293089871
< 0.1%
0.9788001181
< 0.1%
0.97691738611
< 0.1%
0.9767774941
< 0.1%
0.97633963821
< 0.1%
0.97606104611
< 0.1%
0.97368633751
< 0.1%
0.97273898121
< 0.1%
0.97266858821
< 0.1%

Positive affect
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1998
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7094965964
Minimum0.3216897547
Maximum0.9436206222
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2022-07-13T19:40:02.115850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.3216897547
5-th percentile0.5315361917
Q10.6243742555
median0.7206213091
Q30.7991930693
95-th percentile0.8617822558
Maximum0.9436206222
Range0.6219308674
Interquartile range (IQR)0.1748188138

Descriptive statistics

Standard deviation0.1065838287
Coefficient of variation (CV)0.1502245807
Kurtosis-0.6128895537
Mean0.7094965964
Median Absolute Deviation (MAD)0.08549230652
Skewness-0.3496562735
Sum1428.926145
Variance0.01136011254
MonotonicityNot monotonic
2022-07-13T19:40:02.210575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.65122109154
 
0.2%
0.77018315683
 
0.1%
0.74472259282
 
0.1%
0.80282835452
 
0.1%
0.56346319962
 
0.1%
0.72169569732
 
0.1%
0.62002884422
 
0.1%
0.66157654262
 
0.1%
0.52796375482
 
0.1%
0.64548675372
 
0.1%
Other values (1988)1991
98.9%
ValueCountFrequency (%)
0.32168975471
< 0.1%
0.35138705371
< 0.1%
0.36249768731
< 0.1%
0.36943960191
< 0.1%
0.38429245351
< 0.1%
0.38698670271
< 0.1%
0.42096188661
< 0.1%
0.42222747211
< 0.1%
0.42292764781
< 0.1%
0.42412531381
< 0.1%
ValueCountFrequency (%)
0.94362062221
< 0.1%
0.93437367681
< 0.1%
0.92456096411
< 0.1%
0.91893708711
< 0.1%
0.91680097581
< 0.1%
0.91049695011
< 0.1%
0.90617847441
< 0.1%
0.90277212861
< 0.1%
0.90126794581
< 0.1%
0.89981234071
< 0.1%

Negative affect
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2002
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2704943471
Minimum0.08273695409
Maximum0.7045896649
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2022-07-13T19:40:02.314081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.08273695409
5-th percentile0.155018121
Q10.2094505131
median0.2605855316
Q30.3206253499
95-th percentile0.4176633283
Maximum0.7045896649
Range0.6218527108
Interquartile range (IQR)0.1111748368

Descriptive statistics

Standard deviation0.08293012886
Coefficient of variation (CV)0.3065872901
Kurtosis0.9281902378
Mean0.2704943471
Median Absolute Deviation (MAD)0.05407226544
Skewness0.7360933287
Sum544.775615
Variance0.006877406274
MonotonicityNot monotonic
2022-07-13T19:40:02.413682image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.30262704194
 
0.2%
0.28668345512
 
0.1%
0.36514272652
 
0.1%
0.33600781182
 
0.1%
0.26321636392
 
0.1%
0.26220624572
 
0.1%
0.35354433952
 
0.1%
0.34180561352
 
0.1%
0.20651326622
 
0.1%
0.24411874492
 
0.1%
Other values (1992)1992
98.9%
ValueCountFrequency (%)
0.082736954091
< 0.1%
0.092695645991
< 0.1%
0.093412384391
< 0.1%
0.094316124921
< 0.1%
0.095490492881
< 0.1%
0.10349379481
< 0.1%
0.10615817461
< 0.1%
0.10687077791
< 0.1%
0.1083054171
< 0.1%
0.10836613921
< 0.1%
ValueCountFrequency (%)
0.70458966491
< 0.1%
0.64258873461
< 0.1%
0.62222993371
< 0.1%
0.59933549171
< 0.1%
0.59053874021
< 0.1%
0.58126693961
< 0.1%
0.56975805761
< 0.1%
0.56363111731
< 0.1%
0.55709868671
< 0.1%
0.55427873131
< 0.1%

Interactions

2022-07-13T19:39:58.671738image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:50.366602image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:51.334318image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:52.279570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:53.134982image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:54.050936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:55.003028image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:55.926795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:56.780254image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:57.774331image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:58.766459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:50.499407image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:51.422198image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:52.368428image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:53.229701image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:54.139797image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:55.097749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:56.015657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:56.873025image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:57.867102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:58.854344image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:50.587295image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:51.593086image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:52.451430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:53.317584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:54.331192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:55.185631image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:56.098658image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:57.066368image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:57.955963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:58.939296image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:50.685922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:51.674138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:52.533456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:53.405472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:54.411266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:55.274493image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:56.180684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:57.149371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:58.041896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:59.031091image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:50.786498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:51.764951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:52.624272image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:53.502145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:54.501104image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:55.373118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:56.270522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:57.242139image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:58.135637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:59.113118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:50.874387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:51.845026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:52.705322image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:53.589055image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:54.579223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:55.459053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:56.349620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:57.324165image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:58.216690image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:59.207836image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:50.973014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:51.937791image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:52.797113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:53.688658image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:54.671018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:55.558655image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:56.439455image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:57.421818image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:58.310429image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:59.293769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:51.061873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:52.019817image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:52.877183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:53.777519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:54.752064image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:55.648493image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:56.518552image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:57.506771image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:58.394409image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:59.382629image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:51.151708image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:52.106729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:52.961163image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:53.867355image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:54.834092image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:55.740281image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:56.602532image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:57.594657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:58.485224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:59.473445image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:51.243499image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:52.193637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:53.048075image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:53.959146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:54.919049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:55.833049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:56.691394image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:57.684493image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-13T19:39:58.577994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-07-13T19:40:02.503521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-07-13T19:40:02.654879image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-07-13T19:40:02.806237image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-07-13T19:40:02.959547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-07-13T19:39:59.717567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-07-13T19:39:59.907988image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-07-13T19:40:00.009544image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Country nameRegional indicatoryearLadder scoreLogged GDP per capitaSocial supportHealthy life expectancyFreedom to make life choicesGenerosityPerceptions of corruptionPositive affectNegative affect
0AfghanistanSouth Asia20083.7235907.3701000.45066250.7999990.7181140.1676400.8816860.5176370.258195
1AfghanistanSouth Asia20094.4017787.5399720.55230851.2000010.6788960.1900990.8500350.5839260.237092
2AfghanistanSouth Asia20104.7583817.6467090.53907551.5999980.6001270.1205900.7067660.6182650.275324
3AfghanistanSouth Asia20113.8317197.6195320.52110451.9199980.4959010.1624270.7311090.6113870.267175
4AfghanistanSouth Asia20123.7829387.7054790.52063752.2400020.5309350.2360320.7756200.7103850.267919
5AfghanistanSouth Asia20133.5721007.7250290.48355252.5600010.5779550.0611480.8232040.6205850.273328
6AfghanistanSouth Asia20143.1308967.7183540.52556852.8800010.5085140.1040130.8712420.5316910.374861
7AfghanistanSouth Asia20153.9828557.7019920.52859753.2000010.3889280.0798640.8806380.5535530.339276
8AfghanistanSouth Asia20164.2201697.6965600.55907253.0000000.5225660.0422650.7932460.5649530.348332
9AfghanistanSouth Asia20172.6617187.6973810.49088052.7999990.427011-0.1213030.9543930.4963490.371326

Last rows

Country nameRegional indicatoryearLadder scoreLogged GDP per capitaSocial supportHealthy life expectancyFreedom to make life choicesGenerosityPerceptions of corruptionPositive affectNegative affect
2004ZimbabweSub-Saharan Africa20124.9551017.9834680.89647649.5400010.469531-0.1025050.8586910.6692790.177311
2005ZimbabweSub-Saharan Africa20134.6901887.9853910.79927450.9599990.575884-0.1041010.8309370.7118850.182288
2006ZimbabweSub-Saharan Africa20144.1844517.9913350.76583952.3800010.642034-0.0738800.8202170.7252140.239111
2007ZimbabweSub-Saharan Africa20153.7031917.9923390.73580053.7999990.667193-0.1231710.8104570.7150790.178861
2008ZimbabweSub-Saharan Africa20163.7354007.9843720.76842554.4000020.732971-0.0946340.7236120.7376360.208555
2009ZimbabweSub-Saharan Africa20173.6383008.0157380.75414755.0000000.752826-0.0976450.7512080.8064280.224051
2010ZimbabweSub-Saharan Africa20183.6164808.0487980.77538855.5999980.762675-0.0684270.8442090.7101190.211726
2011ZimbabweSub-Saharan Africa20192.6935237.9501320.75916256.2000010.631908-0.0637910.8306520.7160040.235354
2012ZimbabweSub-Saharan Africa20203.1598027.8287570.71724356.7999990.643303-0.0086960.7885230.7025730.345736
2013ZimbabweSub-Saharan Africa20213.1448007.9425950.75047056.2008400.676700-0.0473460.8209990.7177120.224420